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632edb8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | #!/usr/bin/env python3
"""
example.py — Usage demonstrations for hv_quorum_inference.
NumPy only.
"""
import numpy as np
from hv_quorum_inference import (
QuorumConfig, quorum, majority_vote, dominance,
pairwise_divergence, closest_to_centroid,
SyntheticModel,
)
def demo_simple_use():
print("=" * 72)
print("Demo 1 — simplest usage")
print("=" * 72)
print()
# Build a synthetic backend (in production, replace with your LLM wrapper)
model = SyntheticModel(mode_collapse_rate=0.8, seed=0)
prompt = model.make_prompt(concentration=2.0)
cfg = QuorumConfig(
n_samples=64,
max_resamples=1,
dominance_threshold=0.70,
)
# Run quorum
r = quorum(prompt, model, cfg)
print(f" collapse flag: {r.collapsed}")
print(f" candidate: {r.candidate}")
print(f" dominance: {r.final_dominance:.3f}")
print(f" divergence: {r.final_divergence:.3f}")
print(f" samples used: {r.n_samples_used}")
print()
# The correct answer, for reference
print(f" correct answer: {prompt['correct']}")
print()
def demo_clean_vs_collapsed():
print("=" * 72)
print("Demo 2 — clean model vs collapsed model")
print("=" * 72)
print()
cfg = QuorumConfig(n_samples=64, max_resamples=1,
dominance_threshold=0.70)
for label, rate in [('clean (0% collapse)', 0.0),
('moderate (50%)', 0.5),
('severe (80%)', 0.8)]:
model = SyntheticModel(mode_collapse_rate=rate, seed=0)
flag_count = 0
correct_count = 0
for _ in range(50):
prompt = model.make_prompt(concentration=2.0)
r = quorum(prompt, model, cfg)
flag_count += int(r.collapsed)
correct_count += int(model.is_correct(r.candidate, prompt))
print(f" {label}")
print(f" flagged: {flag_count / 50:.2f}")
print(f" correct: {correct_count / 50:.2f}")
print()
def demo_threshold_effect():
print("=" * 72)
print("Demo 3 — how threshold affects flag rate")
print("=" * 72)
print()
print(f" {'threshold':>10s} {'flag@0%':>8s} {'flag@70%':>9s}")
print(" " + "-" * 32)
for th in [0.55, 0.60, 0.65, 0.70, 0.75, 0.80]:
cfg = QuorumConfig(n_samples=64, max_resamples=1,
dominance_threshold=th)
flags = {}
for rate in [0.0, 0.70]:
m = SyntheticModel(mode_collapse_rate=rate, seed=42)
count = 0
for _ in range(100):
p = m.make_prompt(concentration=2.0)
r = quorum(p, m, cfg)
count += int(r.collapsed)
flags[rate] = count / 100
print(f" {th:>10.2f} {flags[0.0]:>8.2f} {flags[0.70]:>9.2f}")
print()
print(" Lower threshold → more flags, more false positives.")
print()
def demo_majority_vs_quorum():
print("=" * 72)
print("Demo 4 — majority vs quorum at 80% collapse")
print("=" * 72)
print()
cfg = QuorumConfig(n_samples=64, max_resamples=1,
dominance_threshold=0.70)
model = SyntheticModel(mode_collapse_rate=0.8, seed=0)
prompt = model.make_prompt(concentration=2.0)
# majority
r_maj = majority_vote(prompt, model, n_samples=64)
print(f" Majority vote:")
print(f" candidate: {r_maj.candidate}")
print(f" correct: {model.is_correct(r_maj.candidate, prompt)}")
print(f" dominance: {r_maj.final_dominance:.3f}")
print()
# quorum (reset RNG for fairness)
model2 = SyntheticModel(mode_collapse_rate=0.8, seed=0)
prompt2 = model2.make_prompt(concentration=2.0)
r_q = quorum(prompt2, model2, cfg)
print(f" Quorum:")
print(f" candidate: {r_q.candidate}")
print(f" collapsed: {r_q.collapsed}")
print(f" dominance: {r_q.final_dominance:.3f}")
print()
print(f" Correct answer: {prompt['correct']}")
print()
def demo_signal_components():
print("=" * 72)
print("Demo 5 — inspect the signal values")
print("=" * 72)
print()
print(f" {'scenario':<24s} {'dominance':>10s} {'divergence':>12s}")
print(" " + "-" * 50)
for label, rate in [('clean', 0.0), ('moderate', 0.4),
('severe', 0.8)]:
model = SyntheticModel(mode_collapse_rate=rate, seed=1)
prompt = model.make_prompt(concentration=2.0)
samples = model.sample(prompt, 64)
emb = model.embed(samples)
dom = dominance(samples)
div = pairwise_divergence(emb)
print(f" {label:<24s} {dom:>10.3f} {div:>12.3f}")
print()
print(" Dominance rises with collapse; divergence falls.")
print(" Either could be a detector. This release uses dominance.")
print()
def demo_custom_backend():
print("=" * 72)
print("Demo 6 — using a custom backend")
print("=" * 72)
print()
# A trivial custom backend that just wraps SyntheticModel
class Wrapped:
def __init__(self, model):
self.model = model
def sample(self, prompt, n):
return self.model.sample(prompt, n)
def embed(self, candidates):
return self.model.embed(candidates)
def is_correct(self, candidate, prompt):
return self.model.is_correct(candidate, prompt)
base = SyntheticModel(mode_collapse_rate=0.6, seed=7)
backend = Wrapped(base)
prompt = base.make_prompt(concentration=2.0)
cfg = QuorumConfig(n_samples=64, max_resamples=1,
dominance_threshold=0.70)
r = quorum(prompt, backend, cfg)
print(f" collapsed: {r.collapsed}")
print(f" candidate: {r.candidate}")
print(f" dominance: {r.final_dominance:.3f}")
print()
print(" To use a real LLM, replace the Wrapped class with a")
print(" HuggingFaceBackend or API wrapper. The quorum algorithm")
print(" only calls sample() and embed().")
print()
def main():
demo_simple_use()
demo_clean_vs_collapsed()
demo_threshold_effect()
demo_majority_vs_quorum()
demo_signal_components()
demo_custom_backend()
if __name__ == "__main__":
main() |